A continuing education resource for registered nurses administering systemic therapy in the outpatient setting
Bibliographic record
Abstract
Background: Systemic therapy administration is a specialized nursing skill that requires advanced education. While initial education for registered nurses (RNs) administering systemic therapy in Newfoundland and Labrador (NL) is well-developed, no formal continuing education resources are available. Comprehensive continuing education assists systemic therapy RNs to maintain their competency, ultimately promoting the health and safety of nurses and patients. Purpose: To develop a continuing education resource for RNs administering systemic therapy in the outpatient setting at the Dr. H. Bliss Murphy Cancer Center. Methods: I conducted a literature review, environmental scan, and consultations with key local stakeholders to explore a lack of continuing education for systemic therapy RNs globally, identify national educational approaches, identify local learning needs, and determine whether a continuing education resource was needed in the local setting. Results: Findings from the literature review, environmental scan, and consultations indicated that a continuing education resource for systemic therapy RNs in the local setting would be beneficial. I developed a comprehensive educational resource that aligns with national systemic therapy nursing practice standards. Conclusion: A continuing education resource for RNs administering systemic therapy in NL should promote maintaining their competency, thus promoting the health and safety of patients and nurses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.098 | 0.026 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".